Vehicle driver assistance systems that enhance the awareness and safety of human drivers and autonomous vehicles increase driver safety and convenience. Autonomous parking and driving are important aspects of autonomous vehicles. However, as with other aspects of autonomous vehicles, autonomous operations such as autonomous parking and driving remain a developing field and improvements in autonomous parking and driving are desirable.
Computer vision systems are an important component of a vehicle driver assistance systems and autonomous vehicles. One type of computer vision system is a stereo computer vision system that includes a front-facing stereo camera that consists of two lenses with a separate image sensor frame for each lens, each directed towards the front of the vehicle. A drawback of stereo computer vision systems is that only a portion of the side view of the vehicle is captured by the stereo camera even if wide angle lenses are used, and that the rear view of the vehicle is not captured by the stereo cameras at all. Although side and rear cameras may be present in some vehicle driver assistance systems and autonomous vehicles, the views from such cameras are typically not combined with the views from the front facing stereo camera and are not part of the computer vision system used for navigation (e.g., parking and driving). Instead, side cameras and rear cameras are used for other purposes such as localization, lane departure detection, collision avoidance, or other functions.
Another type of computer vision system is an omni vision system that consists of either a single monocular camera on a rotating carousel or a single omnidirectional camera. The drawback of omni vision systems is that captured images are nonlinearly highly distorted similar to a convex mirror. Typically, objects located farthest away from the center of the captured image are the most distorted whereas objects located closest to the center of the captured image are the least distorted. When compensating for such image distortion, less image data is available at the center of the captured image due to a high level of compression, resulting in uneven image distortion compensation and a high degree of inaccuracy around the center of the corrected image.
For the foregoing and other reasons, improvements in computer vision systems and alternatives to existing solutions are desirable.